Building Custom Evaluators for AI Applications: A Complete Guide
Pre-built evaluation metrics cover common quality dimensions like accuracy, relevance, and coherence. However, production AI applications require validation against domain-specific business rules, compliance requirements, and proprietary quality standards that generic evaluators cannot assess. Custom evaluators enable teams to enforce these specialized quality checks across AI agent workflows, ensuring applications meet precise business and regulatory requirements.
This guide explains how to build four types of custom evaluators: AI evaluators that leverage LLM reasoning, programmatic evaluators with deterministic logic, API-based evaluators connecting external systems, and human evaluators for expert review. Each approach addresses different validation scenarios, from subjective quality assessment to strict format verification.
Why Custom Evaluators Matter for Enterprise AI
AI evaluation typically begins with standard metrics. Teams measure response accuracy, detect hallucinations, and verify factual grounding using pre-built evaluators. These metrics provide essential baseline quality signals but cannot capture business-specific requirements.
Consider a financial services AI agent. Pre-built evaluators verify factual accuracy and detect hallucinations. However, regulatory compliance requires additional checks: Does every investment recommendation include required risk disclaimers? Does the response avoid providing specific investment advice that requires licensure? Are comparative statements properly qualified? These requirements demand custom evaluation logic tailored to the specific regulatory framework governing financial advice.
Healthcare AI applications face similar constraints. Medical chatbots must caveat limitations, recommend professional consultation for serious symptoms, and avoid diagnostic claims. Legal AI assistants must distinguish between general information and specific legal advice. Customer service agents must adhere to brand voice guidelines and escalation policies specific to the organization.
Custom evaluators transform these business rules into automated quality checks that run against every agent output, ensuring compliance without manual review bottlenecks.
Custom AI Evaluators: LLM-as-a-Judge for Subjective Assessment
Custom AI evaluators use LLMs to assess outputs based on natural language instructions. This approach excels at evaluating subjective quality dimensions that resist programmatic measurement: tone appropriateness, brand voice compliance, nuanced safety guidelines, and contextual relevance.
Configuration Process
Building a custom AI evaluator begins in the Evaluators Library. Teams select the judge model from available options including GPT-4, Claude 3.5 Sonnet, or other LLMs. The choice of judge model matters. More capable models provide better reasoning about complex criteria but increase evaluation costs and latency.
The evaluation instructions define how the judge model assesses outputs. These instructions inject dynamic values using template variables:
{{input}}- The prompt sent to your agent{{output}}- The response generated by your agent{{context}}- Retrieved context or RAG documents{{expected_output}}- Ground truth from your dataset
For example, a brand voice evaluator might receive instructions: "Evaluate whether the {{output}} maintains our brand's professional yet approachable tone. The response should avoid jargon, use active voice, and demonstrate empathy when addressing customer concerns. Return 'Compliant' if the response meets all criteria, 'Minor Violation' if one criterion is missed, or 'Major Violation' if multiple criteria are violated."
Scoring Configuration
Custom AI evaluators support three scoring formats:
Binary scoring returns True/False or Pass/Fail verdicts. This format suits compliance checks with clear pass/fail criteria. Example: "Does the response include the required data privacy disclaimer?"
Scale scoring returns numeric ratings, typically 1-5 Likert scales. This format captures quality gradations for criteria like helpfulness, clarity, or professionalism. The judge model's instructions specify what each score level represents.
Categorical scoring returns specific string labels defining qualitative categories. Example labels: "Safe," "Needs Review," "Unsafe" for content moderation, or "Excellent," "Good," "Acceptable," "Poor" for quality assessment.
Pass criteria configuration defines success thresholds. For scale scoring, teams set minimum acceptable scores (e.g., "Score must be ≥ 4"). For categorical scoring, pass criteria specify acceptable categories (e.g., "Must return 'Safe' or 'Needs Review'").
Use Cases for AI Evaluators
Custom AI evaluators work best when evaluation requires reasoning about context, nuance, or subjective quality:
Regulatory compliance: Verify responses include required disclaimers, avoid prohibited claims, and adhere to industry-specific communication standards. Financial services, healthcare, and legal applications benefit significantly from these checks.
Brand voice consistency: Ensure agent responses match established brand personality across tone, vocabulary, formality level, and communication style. Consumer-facing applications require consistent brand representation.
Content safety: Assess responses for subtle safety issues that keyword matching cannot detect. Evaluators can identify responses that appear helpful but contain misleading health advice, investment recommendations requiring licensure, or inappropriately personal questions.
Task completion validation: Evaluate whether multi-turn conversations successfully resolved user requests. AI evaluators can assess conversation quality holistically across multiple exchanges.
Custom Programmatic Evaluators: Deterministic Logic for Precise Validation
Programmatic evaluators execute code-based validation logic in Python or JavaScript. This approach provides deterministic, reproducible quality checks for format requirements, data structure validation, and business rules that code can verify objectively.
Implementation Requirements
Programmatic evaluators require a validate function accepting standard arguments and returning results matching the configured response type (Boolean, Number, or String).
Function signature:
def validate(input, output, expected_output, context, **kwargs):
# Validation logic
return result
The function receives the same template variables available to AI evaluators. Return values must match the evaluator's response type configuration:
- Boolean evaluators return True/False
- Numeric evaluators return integers or floats
- String evaluators return text labels
Example: Sentence Count Validation
This programmatic evaluator verifies outputs contain sufficient detail by checking sentence count:
def validate(input, output, expected_output, context, **kwargs):
# Split output into sentences
sentences = output.split('.')
# Filter empty strings from split
valid_sentences = [s for s in sentences if s.strip()]
if len(valid_sentences) < 3:
# Return False for "Fail" if Binary type selected
return False
# Return True for "Pass"
return True
This simple example demonstrates core principles. Production evaluators implement more sophisticated logic: JSON schema validation, regex pattern matching, API response format verification, or complex business rule enforcement.
Advanced Programmatic Use Cases
Format validation: Verify outputs conform to required structures. Examples include validating product SKU formats (e.g., "Must match pattern SKU-####"), checking email addresses use proper domains, or ensuring phone numbers follow regional formats.
Data completeness: Confirm responses include all required fields. Customer service agents must capture specific information (order numbers, account details) before escalating tickets. Programmatic evaluators verify these requirements are met.
Keyword enforcement: Check for presence or absence of specific terms. Financial compliance might prohibit certain competitive references. Medical applications might require specific disclaimers appear in responses.
Logical consistency: Validate relationships between data points. If an agent provides a discount code, verify the code matches current promotions. If recommending products, confirm inventory availability from context data.
Programmatic evaluators excel when validation logic is explicit and deterministic. Code provides faster execution and lower costs than LLM-based evaluation for these scenarios.
API-Based Evaluators: Connecting External Validation Systems
Organizations often maintain existing evaluation infrastructure: proprietary scoring models, specialized compliance APIs, or domain-specific quality assessment systems. API-based evaluators integrate these external systems into the evaluation workflow, maintaining unified quality visibility while leveraging established validation tools.
Configuration Components
API-based evaluators require several configuration elements:
Endpoint URL: The external API that receives evaluation requests and returns scores or verdicts.
HTTP method: Typically POST for sending payload data, though GET works for stateless lookups.
Headers: Authentication tokens, API keys, content type specifications, and custom headers required by the external service.
Payload structure: Mapping between evaluation variables (input, output, context, expected_output) and the external API's expected request format.
Response parsing: Instructions for extracting scores or verdicts from the API response. The evaluator maps response fields to the configured result type.
Integration Scenarios
Proprietary compliance systems: Organizations in regulated industries often build internal compliance checking systems. API evaluators connect these systems to the AI evaluation pipeline, ensuring every agent output passes existing compliance validation.
Third-party content moderation: Specialized services detect nuanced safety issues like subtle bias, cultural insensitivity, or content policy violations. API evaluators leverage these services without reimplementing detection logic.
Domain-specific quality models: Teams with custom quality prediction models can expose them via API and integrate as evaluators. Machine learning models trained on domain-specific quality data provide automated assessment tailored to specific application requirements.
External knowledge bases: Factual verification against authoritative databases requires API integration. Medical fact-checking against clinical databases, product information verification against inventory systems, or pricing validation against rate tables all benefit from API-based evaluation.
API evaluators maintain separation between evaluation infrastructure and AI application code, allowing independent evolution of both systems.
Human Evaluators: Expert Review for High-Stakes Validation
Automated evaluators handle scale efficiently but cannot replace human judgment for subjective quality assessment, nuanced safety evaluation, or high-stakes decision validation. Human-in-the-loop evaluation incorporates expert review into systematic quality assessment.
Configuration and Workflow
Human evaluator configuration defines the review interface and guidelines:
Rating interface: Specify how reviewers assess outputs. Options include Likert scales (1-5 ratings), binary approval/rejection, categorical classifications, or free-text feedback fields.
Review guidelines: Provide detailed instructions explaining evaluation criteria, edge case handling, and decision frameworks. Clear guidelines ensure consistent assessment across multiple reviewers.
Task assignment: Configure which team members receive review tasks and how tasks are distributed. Review workload balances across available subject matter experts.
Human Evaluation Use Cases
Subjective quality dimensions: Automated metrics struggle with creativity, humor appropriateness, or emotional intelligence. Human reviewers assess whether responses demonstrate genuine understanding and appropriate emotional tenor.
Cultural sensitivity: AI outputs require review for subtle cultural references, regional appropriateness, and inclusive language that automated systems may miss.
High-stakes decisions: Medical advice, legal guidance, or financial recommendations warrant expert human review before deployment, even when automated evaluators show strong performance.
Ground truth creation: Human review generates labeled data for training custom AI evaluators. Initial human assessment establishes quality standards that LLM-as-a-judge evaluators learn to replicate.
Evaluation calibration: Human review validates automated evaluator accuracy. Periodic human assessment confirms AI evaluators and programmatic checks maintain alignment with actual quality standards.
Human evaluators create quality bottlenecks by design, ensuring critical outputs receive expert scrutiny before reaching users.
Testing and Validation: The Evaluator Playground
Before deploying custom evaluators in production workflows, teams validate that evaluation logic performs correctly. The Evaluator Playground provides immediate feedback during evaluator development.
Testing Process
The Playground appears on the right side of the evaluator editor during configuration. Testing follows a simple workflow:
- Enter sample values: Provide representative examples for template variables (input, output, context, expected_output)
- Execute evaluation: Click Run to process the sample data through the evaluator
- Review results: Examine the returned score, verdict, and reasoning trace
For AI evaluators, the reasoning trace shows how the judge model interpreted evaluation instructions and reached its verdict. This visibility helps teams refine instructions when evaluators produce unexpected results.
For programmatic evaluators, testing verifies code executes without errors and returns results in the expected format. Teams test edge cases: empty strings, special characters, unusual formats, and boundary conditions.
Multiple test iterations with diverse examples ensure evaluators handle the full range of production scenarios correctly. Teams should test both passing and failing cases to confirm evaluators distinguish quality levels accurately.
Best Practices for Custom Evaluator Development
Start with clear success criteria: Define precisely what constitutes passing outputs before writing evaluation logic. Vague criteria produce inconsistent evaluation.
Test comprehensively: Evaluate the evaluator using diverse examples covering normal cases, edge cases, and failure modes. Untested evaluators often fail in production.
Monitor evaluator performance: Track how often custom evaluators run, their pass rates, and execution costs. Unexpectedly low or high pass rates indicate misconfigured evaluation logic.
Version evaluation logic: As business requirements evolve, evaluators need updates. Version control enables tracking changes to evaluation criteria over time.
Combine evaluator types: Complex quality assessment often requires multiple evaluators. AI evaluators assess subjective dimensions while programmatic evaluators enforce format requirements. API evaluators verify external compliance while human reviewers provide final validation.
Optimize costs: AI evaluators using expensive judge models increase evaluation costs significantly. Use simpler models for straightforward criteria, reserving sophisticated models for complex reasoning tasks.
Document evaluation criteria: Write clear documentation explaining what each evaluator checks and why. This helps team members understand quality standards and maintain evaluators as requirements change.
Conclusion
Custom evaluators transform business requirements into automated quality checks that scale across AI applications. AI evaluators leverage LLM reasoning for subjective assessment. Programmatic evaluators enforce deterministic business rules. API evaluators integrate external validation systems. Human evaluators provide expert review for high-stakes scenarios.
Production AI applications combine multiple evaluator types, creating comprehensive quality frameworks that catch issues across multiple dimensions. Maxim's evaluation platform provides unified infrastructure for building, testing, and deploying custom evaluators alongside pre-built metrics.
Teams shipping reliable AI agents need flexible evaluation systems that adapt to evolving business requirements. Custom evaluators ensure AI applications meet domain-specific quality standards from development through production deployment.
Ready to build custom evaluators for your AI applications? Get started with Maxim or schedule a demo to see how comprehensive evaluation accelerates AI development.
